Uncover the nature of overlapping community in cities
This work provides a novel geospatial perspective on urban structures, uncovering segregation patterns in U.S. cities, though it is incremental in applying a graph-based deep learning framework to this domain.
The study tackled the problem of understanding overlapping communities in urban spaces by analyzing mobile phone positioning data from the Twin Cities metro area, revealing that 95.7% of urban functional complexity arises from overlapping community structures during weekdays and showing correlations with income and racial indicators.
Urban spaces, though often perceived as discrete communities, are shared by various functional and social groups. Our study introduces a graph-based physics-aware deep learning framework, illuminating the intricate overlapping nature inherent in urban communities. Through analysis of individual mobile phone positioning data at Twin Cities metro area (TCMA) in Minnesota, USA, our findings reveal that 95.7 % of urban functional complexity stems from the overlapping structure of communities during weekdays. Significantly, our research not only quantifies these overlaps but also reveals their compelling correlations with income and racial indicators, unraveling the complex segregation patterns in U.S. cities. As the first to elucidate the overlapping nature of urban communities, this work offers a unique geospatial perspective on looking at urban structures, highlighting the nuanced interplay of socioeconomic dynamics within cities.